mirror of https://github.com/python/cpython
SF patch 629637: Add sample(population, k) method to the random module.
Used for random sampling without replacement.
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@ -179,6 +179,25 @@ Functions for sequences:
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long sequence can never be generated.
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\end{funcdesc}
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\begin{funcdesc}{sample}{population, k}
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Return a \var{k} length list of unique elements chosen from the
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population sequence. Used for random sampling without replacement.
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Returns a new list containing elements from the population. The
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list itself is in random order so that all sub-slices are also
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random samples. The original sequence is left undisturbed.
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If the population has repeated elements, then each occurence is a
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possible selection in the sample.
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If indices are needed for a large population, use \function{xrange}
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as an argument: \code{sample(xrange(10000000), 60)}.
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Optional argument random is a 0-argument function returning a random
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float in [0.0, 1.0); by default, the standard random.random.
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\versionadded{2.3}
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\end{funcdesc}
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The following functions generate specific real-valued distributions.
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Function parameters are named after the corresponding variables in the
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@ -7,6 +7,7 @@
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sequences
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---------
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pick random element
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pick random sample
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generate random permutation
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distributions on the real line:
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@ -77,7 +78,7 @@ from math import log as _log, exp as _exp, pi as _pi, e as _e
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from math import sqrt as _sqrt, acos as _acos, cos as _cos, sin as _sin
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from math import floor as _floor
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__all__ = ["Random","seed","random","uniform","randint","choice",
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__all__ = ["Random","seed","random","uniform","randint","choice","sample",
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"randrange","shuffle","normalvariate","lognormvariate",
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"cunifvariate","expovariate","vonmisesvariate","gammavariate",
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"stdgamma","gauss","betavariate","paretovariate","weibullvariate",
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@ -373,6 +374,43 @@ class Random:
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j = int(random() * (i+1))
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x[i], x[j] = x[j], x[i]
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def sample(self, population, k, random=None, int=int):
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"""Chooses k unique random elements from a population sequence.
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Returns a new list containing elements from the population. The
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list itself is in random order so that all sub-slices are also
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random samples. The original sequence is left undisturbed.
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If the population has repeated elements, then each occurence is
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a possible selection in the sample.
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If indices are needed for a large population, use xrange as an
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argument: sample(xrange(10000000), 60)
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Optional arg random is a 0-argument function returning a random
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float in [0.0, 1.0); by default, the standard random.random.
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"""
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n = len(population)
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if not 0 <= k <= n:
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raise ValueError, "sample larger than population"
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if random is None:
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random = self.random
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if n < 6 * k: # if n len list takes less space than a k len dict
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pool = list(population)
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for i in xrange(n-1, n-k-1, -1):
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j = int(random() * (i+1))
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pool[i], pool[j] = pool[j], pool[i]
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return pool[-k:]
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inorder = [None] * k
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selections = {}
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for i in xrange(k):
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j = int(random() * n)
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while j in selections:
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j = int(random() * n)
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selections[j] = inorder[i] = population[j]
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return inorder # return selections in the order they were picked
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## -------------------- real-valued distributions -------------------
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## -------------------- uniform distribution -------------------
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@ -711,7 +749,19 @@ def _test_generator(n, funccall):
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print 'avg %g, stddev %g, min %g, max %g' % \
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(avg, stddev, smallest, largest)
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def _test(N=20000):
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def _test_sample(n):
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# For the entire allowable range of 0 <= k <= n, validate that
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# the sample is of the correct length and contains only unique items
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population = xrange(n)
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for k in xrange(n+1):
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s = sample(population, k)
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assert len(dict([(elem,True) for elem in s])) == len(s) == k
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def _sample_generator(n, k):
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# Return a fixed element from the sample. Validates random ordering.
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return sample(xrange(n), k)[k//2]
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def _test(N=2000):
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print 'TWOPI =', TWOPI
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print 'LOG4 =', LOG4
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print 'NV_MAGICCONST =', NV_MAGICCONST
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@ -735,6 +785,9 @@ def _test(N=20000):
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_test_generator(N, 'betavariate(3.0, 3.0)')
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_test_generator(N, 'paretovariate(1.0)')
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_test_generator(N, 'weibullvariate(1.0, 1.0)')
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_test_generator(N, '_sample_generator(50, 5)') # expected s.d.: 14.4
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_test_generator(N, '_sample_generator(50, 45)') # expected s.d.: 14.4
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_test_sample(1000)
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# Test jumpahead.
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s = getstate()
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@ -760,6 +813,7 @@ uniform = _inst.uniform
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randint = _inst.randint
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choice = _inst.choice
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randrange = _inst.randrange
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sample = _inst.sample
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shuffle = _inst.shuffle
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normalvariate = _inst.normalvariate
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lognormvariate = _inst.lognormvariate
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@ -427,6 +427,9 @@ Library
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- Added operator.pow(a,b) which is equivalent to a**b.
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- Added random.sample(population,k) for random sampling without replacement.
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Returns a k length list of unique elements chosen from the population.
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- random.randrange(-sys.maxint-1, sys.maxint) no longer raises
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OverflowError. That is, it now accepts any combination of 'start'
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and 'stop' arguments so long as each is in the range of Python's
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